We will establish a quantification method for neonatal brain MRI to evaluate the abnormalities of preterm born neonates. In the U.S., approximately 12% of all neonates are born preterm (<37 weeks gestation), with 2% of these being very preterm (VPB, 28-32 weeks gestation). The percentage of preterm births has been increasing over the last ten years, partly due to improved neonatal care for preterm infants. Nevertheless, about half of the VPB infants may develop clinically-evident neurological or psychological disorders and the number could be even higher if subtle functional abnormalities are included. The extent of neurocognitive deficits in the late preterm infants (33-36 weeks gestation) is also unknown. However, most of the neuro- cognitive deficits are not easily detected during the first year of life. To benefit from early intervention and to develop more deficits-specific interventions, we need methods to detect and quantify brain abnormalities at an early stage. MRI is one of the most promising and sensitive methods to detect subtle anatomic abnormalities in the neonatal brain. Previous brain MRI studies have found some correlations between several types of abnormalities and neurological outcomes, but there are also reports that found no relationship between signal alterations and neurological outcomes. Hence, the current knowledge does not justify the use of MRI for routine clinical evaluations. To optimize the usefulness of MRI for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is needed. The proposed method is based on two core technologies for the quantification of neonatal brain anatomy: a deformable brain atlas with detailed anatomic information and a highly elastic topology-preserved warping method. The combination will provide multiple MR parameters from 176 automatically segmented brain structures. The goals of this project are to establish an atlas-based, automated quantification method for the neonatal brain, to evaluate the detail anatomy of premature neonates at a term-equivalent age. The overall hypothesis is that our DTI-guided quantitative brain analysis will sensitively detect anatomical abnormalities of preterm born neonates in region- specific manner. We have four specific aims.
In Aim 1, we will create a multi-contrast (T1-, T2-weighted, and DTI) normal-term neonatal brain atlas for quantitative brain analysis, which will be a statistical representation of the population (""""""""Bayesian atlas"""""""").
In Aim 2, we will combine the Bayesian atlas with highly elastic topology- preserved warping (Large Deformation Diffeomorphic Metric Mapping, LDDMM) for automated brain segmentation and test the segmentation accuracy.
In Aim 3, we will use the combination of the Bayesian atlas and LDDMM to perform T1/T2/DTI quantification of term neonatal brain MRIs.
In Aim 4, we will apply the method to the brain MRIs from term-equivalent preterm born infants (born at 28-36 weeks gestational age) and compare the MR parameters to those in the term infants. This study will be a first step toward seeking very early prognostic indicators for functional outcomes of the anatomical brain abnormalities in preterm births.

Public Health Relevance

We will establish an automated quantification method for neonatal brain MRI to evaluate the brain anatomical abnormalities of preterm born neonates. The number of very preterm born babies is increasing in the US, partly due to improved neonatal intensive care for these babies, and about half of these infants develop neurological or psychiatric disorders. We believe that this proposed MRI method will improve the diagnosis and hence early intervention for treatments of preterm born neonates and pediatric patients.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Research Project (R01)
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Special Emphasis Panel (ZRG1-NT-B (08))
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Raju, Tonse N
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Johns Hopkins University
Schools of Medicine
United States
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Wu, Dan; Chang, Linda; Akazawa, Kentaro et al. (2017) Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI. Neuroimage 149:33-43
Akazawa, Kentaro; Chang, Linda; Yamakawa, Robyn et al. (2016) Probabilistic maps of the white matter tracts with known associated functions on the neonatal brain atlas: Application to evaluate longitudinal developmental trajectories in term-born and preterm-born infants. Neuroimage 128:167-179
Chang, Linda; Akazawa, Kentaro; Yamakawa, Robyn et al. (2016) Delayed early developmental trajectories of white matter tracts of functional pathways in preterm-born infants: Longitudinal diffusion tensor imaging data. Data Brief 6:1007-15
Davis, Cameron L; Oishi, Kenichi; Faria, Andreia V et al. (2016) White matter tracts critical for recognition of sarcasm. Neurocase 22:22-9
Faria, Andreia V; Oishi, Kenichi; Yoshida, Shoko et al. (2015) Content-based image retrieval for brain MRI: an image-searching engine and population-based analysis to utilize past clinical data for future diagnosis. Neuroimage Clin 7:367-76
Oishi, Kenichi; Faria, Andreia V; Hsu, John et al. (2015) Critical role of the right uncinate fasciculus in emotional empathy. Ann Neurol 77:68-74
Deshpande, Rajiv; Chang, Linda; Oishi, Kenichi (2015) Construction and application of human neonatal DTI atlases. Front Neuroanat 9:138
Zhang, Yajing; Zhang, Jiangyang; Hsu, Johnny et al. (2014) Evaluation of group-specific, whole-brain atlas generation using Volume-based Template Estimation (VTE): application to normal and Alzheimer's populations. Neuroimage 84:406-19
Zhang, Yajing; Chang, Linda; Ceritoglu, Can et al. (2014) A Bayesian approach to the creation of a study-customized neonatal brain atlas. Neuroimage 101:256-67
Oishi, Kenichi; Faria, Andreia V; Yoshida, Shoko et al. (2013) Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging. Int J Dev Neurosci 31:512-24

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